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A Conceptual Approach for the Knowledge-Based Computational Design of Prefabricated Façade Panels Using Constructability Featuresdil;ade Panels Using Constructability Features Zadeh, Puyan A.; Diaz, Santiago; Staub-French, Sheryl; Bhonde, Devarsh
Abstract
The use of parametric models in the architecture, engineering, and construction (AEC) industry has made it possible to create complex and creative building designs. However, this design complexity creates major constructability issues, especially in projects that incorporate prefabricated façade panels. Computational design methods can solve some of these issues; however, such methods do not necessarily include the systematic approach to integrating domain knowledge, which results in inefficiencies in the design and construction processes. This paper introduces how constructability knowledge can be incorporated into computational design process using feature-based modeling (FBM). An ethnographic case study of a high-rise building with complex façade design is presented in this paper. The research identifies the critical geometric constraints that affect constructability and introduces a new three-level taxonomy (Micro, Meso, Macro) for classifying these constraints. The suggested taxonomy is then applied to inform developing a conceptual knowledge-based computational design approach that enables incorporating the insights of domain experts into the design process. Moreover, the research provides a range of external examples to validate the proposed taxonomy. The findings demonstrate the potential of FBM to streamline the design and fabrication of prefabricated façade panels, improving constructability without compromising architectural intent. This study provides a structured methodology that can be applied to enhance design efficiency and reduce construction risks in similar projects.
Item Metadata
Title |
A Conceptual Approach for the Knowledge-Based Computational Design of Prefabricated Façade Panels Using Constructability Featuresdil;ade Panels Using Constructability Features
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Creator | |
Publisher |
Multidisciplinary Digital Publishing Institute
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Date Issued |
2025-02-15
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Description |
The use of parametric models in the architecture, engineering, and construction (AEC) industry has made it possible to create complex and creative building designs. However, this design complexity creates major constructability issues, especially in projects that incorporate prefabricated façade panels. Computational design methods can solve some of these issues; however, such methods do not necessarily include the systematic approach to integrating domain knowledge, which results in inefficiencies in the design and construction processes. This paper introduces how constructability knowledge can be incorporated into computational design process using feature-based modeling (FBM). An ethnographic case study of a high-rise building with complex façade design is presented in this paper. The research identifies the critical geometric constraints that affect constructability and introduces a new three-level taxonomy (Micro, Meso, Macro) for classifying these constraints. The suggested taxonomy is then applied to inform developing a conceptual knowledge-based computational design approach that enables incorporating the insights of domain experts into the design process. Moreover, the research provides a range of external examples to validate the proposed taxonomy. The findings demonstrate the potential of FBM to streamline the design and fabrication of prefabricated façade panels, improving constructability without compromising architectural intent. This study provides a structured methodology that can be applied to enhance design efficiency and reduce construction risks in similar projects.
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Subject | |
Genre | |
Type | |
Language |
eng
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Date Available |
2025-05-02
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Provider |
Vancouver : University of British Columbia Library
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Rights |
CC BY 4.0
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DOI |
10.14288/1.0448719
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URI | |
Affiliation | |
Citation |
Applied Sciences 15 (4): 2035 (2025)
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Publisher DOI |
10.3390/app15042035
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Peer Review Status |
Reviewed
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Scholarly Level |
Faculty; Researcher
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
CC BY 4.0